Resource Allocation for Cellular-based Inter-Vehicle Communications in Autonomous Multiplatoons

Peng, Haibing; Li, Dazhou; Ye, Qiang; Abboud, Khadige; Zhang, Hai; Zhuang, Weihua; Shen, Xuemin

doi:10.1109/tvt.2017.2723430

Cited by 105 publications

(51 citation statements)

References 29 publications

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“…Similar to the free ADVNETs, infrastructures would play the control role either for network or platoons in most of platoon-based ADVNETs. Hence, in the infrastructure-based environments, infrastructures are usually considered to relay information for AVs [90], [91].…”

Section: A Communication Structuresmentioning

confidence: 99%

Vehicular Communications: A Network Layer Perspective

Peng

Liang

Shen

et al. 2019

IEEE Trans. Veh. Technol.

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243

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Vehicular communications, referring to information exchange among vehicles, pedestrians, and infrastructures, have become very popular and been widely studied recently due to its great potential to support intelligent transportation and various safety applications. Via vehicular communications, manually driving vehicles and autonomous vehicles can collect useful information to improve traffic safety and support infotainment services. In this paper, we provide a comprehensive overview of recent research on enabling efficient vehicular communications from the network layer perspective. First, we introduce general applications and unique characteristics of vehicular networks and the corresponding classifications. Based on different driving patterns of vehicles, we divide vehicular networks into two categories, i.e., manually driving vehicular networks and automated driving vehicular networks, and then discuss the available communication techniques, network structures, routing protocols, and handoff strategies applied in these vehicular networks. Finally, we identify the challenges confronted by the current vehicular communications and present the corresponding research opportunities. Index TermsVehicular communications; manually driving vehicles; autonomous vehicles; routing protocols; handoff strategies; communication technologies. from the perspective of the physical layer in [1]. Via enabling vehicles to exchange information with other vehicles (i.e., vehicle-to-vehicle, V2V), pedestrians (i.e., vehicle-to-pedestrian, V2P), and infrastructures (i.e., vehicle-to-infrastructure, V2I), vehicular communications are expected to support a variety of applications, including intelligent transportation and safety applications [2], [3]. However, due to the high mobility and complicated communications environments, it is very challenging to provide efficient vehicular communications to satisfy the different requirements, especially, higher reliability and lower latency for sharing safety-related information.Manually driving vehicular networks (MDVNETs), referring to communications among vehicles with only manually driving patterns, are one of the main applications of vehicular communications and have been widely considered in the existing works [4]. MDVNETs can help manually driving vehicles to improve the traffic safety and provide infotainment services to drivers and passengers [3].Owing to the advances in sensor technologies, wireless communications, computational power, and intelligent control, a new driving pattern, named automated driving, is gradually applied in vehicles, and then autonomous vehicles (AVs) are generated and begin to be test-driven on the roads. According to a report from the National Highway Safety Administration (NHTSA) [5], passenger vehicles can be classified into five distinct levels of autonomy, as shown in Table I.Manually driving vehicles mentioned in this paper refer to the vehicles with level 0 autonomy while AVs refer to vehicles with level 4 autonomy. It is expected that no human actions o...

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Section: A Communication Structuresmentioning

confidence: 99%

Vehicular Communications: A Network Layer Perspective

Peng

Liang

Shen

et al. 2019

IEEE Trans. Veh. Technol.

Self Cite

243

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“…In [ 8 ], Fernandes and Nunes proposed a multi-platooning leaders positioning strategy and an algorithm to maintain a constant inter-platoon spacing and attain high traffic capacity. In [ 10 ], Peng et al presented a sub-channel allocation scheme by jointly considering the broadcast services and device-to-device (D2D) communications. This scheme reduced the end-to-end delay and increased the successful delivery ratio in a multi-platoon.…”

Section: Related Workmentioning

confidence: 99%

A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications

Nie

Fan

et al. 2018

Sensors

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Multi-platooning is an important management strategy for autonomous driving technology. The backbone vehicles in a multi-platoon adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit vehicles’ kinematics information through inter-platoon communications, and then forward the information to the member vehicles through intra-platoon communications. In this case, each vehicle in a multi-platoon can acquire the kinematics information of other vehicles. The parameters of DCF, the hidden terminal problem and the number of neighbors may incur a long and unbalanced one-hop delay of inter-platoon communications, which would further prolong end-to-end delay of inter-platoon communications. In this case, some vehicles within a multi-platoon cannot acquire the emergency changes of other vehicles’ kinematics within a limited time duration and take prompt action accordingly to keep a multi-platoon formation. Unlike other related works, this paper proposes a swarming approach to optimize the one-hop delay of inter-platoon communications in a multi-platoon scenario. Specifically, the minimum contention window size of each backbone vehicle is adjusted to enable the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay. The simulation results indicate that, the one-hop delay of the proposed approach is reduced by 12% as compared to the DCF mechanism with the IEEE standard contention window size. Moreover, the end-to-end delay, one-hop throughput, end-to-end throughput and transmission probability have been significantly improved.

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“…The relationship between the actual speed and expeted speed of the automated vehicles can be approximated as [7]:…”

Section: Macroscopic Traffic Flow Differential Equation Model For Autmentioning

confidence: 99%

Modeling and simulations on automated vehicles to alleviate traffic congestion

Chang¹,

Wang²,

Xiao³

et al. 2017

Math. models, eng.

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Abstract. Automated vehicle technologies have the potential to improve traffic efficiency, stability and safety. Some models of mixed traffic are presented, which consist of automated vehicles and regular vehicles. First, a differential equation model based on automated vehicles is developed by regarding the traffic flow as a whole and using three continuous variables (traffic density, driving speed and traffic volume) to reflect the basic characteristics of traffic from the macroscopic perspective. Second, by taking into account (1) Actual Expected Speed Ratio (AESR), (2) Change Lane Count (CL), (3) Backward Distance (BD), and (4) Over Take (OT), the traffic flow evaluation model is established. Furthermore, by selecting locations randomly, we take the average value of the five cellular automata to the simulating results and validate the reliability of the model in the tolerance. Finally, optimal solutions to accommodate automated vehicles are given.

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Resource Allocation for Cellular-based Inter-Vehicle Communications in Autonomous Multiplatoons

Cited by 105 publications

References 29 publications

Vehicular Communications: A Network Layer Perspective

Vehicular Communications: A Network Layer Perspective

A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications

Modeling and simulations on automated vehicles to alleviate traffic congestion

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